google/uncertainty-baselines

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1592 stars · 223 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A library of high-quality, minimal-dependency implementations of standard and state-of-the-art uncertainty and robustness methods for deep learning, built on TensorFlow. It serves as a template for researchers to benchmark and prototype new ideas against consistent baselines.

Use cases

• benchmark uncertainty estimation methods on CIFAR and ImageNet
• reproduce SOTA Bayesian deep learning baselines
• prototype new uncertainty or robustness ideas on top of standard baselines
• compare deterministic vs probabilistic neural network training
• run uncertainty experiments on TPUs via Colab or Google Cloud
• fork a baseline training script for a paper

When to choose

• you research uncertainty quantification or robustness in deep learning
• you need consistent, comparable baselines for a paper
• you want forkable TensorFlow training scripts with minimal interdependencies
• you need TPU-ready experiment setups

When to avoid

• you need a stable released API for production
• you work in PyTorch rather than TensorFlow
• you need general-purpose ML tooling unrelated to uncertainty
• you want a maintained pip-installable stable version

Facets

library · maturity active

machine-learning deep-learning benchmarking testing machine-learning deep-learning data-science python cloud uncertainty-quantification bayesian-deep-learning tensorflow robustness research-baselines probabilistic-modeling gpu

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For agents

markdown · JSON · MCP: product_card(name="google/uncertainty-baselines")